






"""simple docstring"""
import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


snake_case : Any     		=		get_logger(__name__)


snake_case : Dict     		=		r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""



class        UpperCamelCase__ :







    """simple docstring"""







    @add_start_docstrings(UpperCamelCase_ )
    def __call__(		self :	List[Any]				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray ):





           '''simple docstring'''

           raise NotImplementedError(
               f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )



class        UpperCamelCase__ :







    """simple docstring"""







    @add_start_docstrings(UpperCamelCase_ )
    def __call__(		self :	List[Any]				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray ):





           '''simple docstring'''

           raise NotImplementedError(
               f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    @add_start_docstrings(UpperCamelCase_ )
    def __call__(		self :	Any				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	int				,    **UpperCamelCase_ :	str ):





           '''simple docstring'''

           for processor in self:
                  __magic_name__      				=    inspect.signature(processor.__call__ ).parameters
                  if len(UpperCamelCase_ ) > 3:
                         if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
                                raise ValueError(
                                    f"""Make sure that all the required parameters: {list(function_args.keys() )} for """
                                    f"""{processor.__class__} are passed to the logits processor.""" )
                         __magic_name__      				=    processor(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_ )
                  else:
                         __magic_name__      				=    processor(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )
           return scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	Any				,    UpperCamelCase_ :	float ):





           '''simple docstring'''

           if not isinstance(UpperCamelCase_				,    UpperCamelCase_ ) or not (temperature > 0):
                  raise ValueError(f"""`temperature` has to be a strictly positive float, but is {temperature}""" )

           __magic_name__      				=    temperature







    def __call__(		self :	Any				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    scores / self.temperature
           return scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	List[str]				,    UpperCamelCase_ :	float				,    UpperCamelCase_ :	float = -float('Inf' )				,    UpperCamelCase_ :	int = 1 ):





           '''simple docstring'''

           if not isinstance(UpperCamelCase_				,    UpperCamelCase_ ) or (top_p < 0 or top_p > 1.0):
                  raise ValueError(f"""`top_p` has to be a float > 0 and < 1, but is {top_p}""" )
           if not isinstance(UpperCamelCase_				,    UpperCamelCase_ ) or (min_tokens_to_keep < 1):
                  raise ValueError(f"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}""" )

           __magic_name__      				=    top_p
           __magic_name__      				=    filter_value
           __magic_name__      				=    min_tokens_to_keep







    def __call__(		self :	Dict				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    lax.top_k(UpperCamelCase_				,    scores.shape[-1] )

           __magic_name__      				=    jnp.full_like(UpperCamelCase_				,    self.filter_value )
           __magic_name__      				=    jax.nn.softmax(UpperCamelCase_				,    axis=-1 ).cumsum(axis=-1 )
           __magic_name__      				=    cumulative_probs < self.top_p

           # include the token that is higher than top_p as well
           __magic_name__      				=    jnp.roll(UpperCamelCase_				,    1 )
           score_mask |= score_mask.at[:, 0].set(UpperCamelCase_ )

           # min tokens to keep
           __magic_name__      				=    score_mask.at[:, : self.min_tokens_to_keep].set(UpperCamelCase_ )

           __magic_name__      				=    jnp.where(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )
           __magic_name__      				=    jax.lax.sort_key_val(UpperCamelCase_				,    UpperCamelCase_ )[-1]

           return next_scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	int				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	float = -float('Inf' )				,    UpperCamelCase_ :	int = 1 ):





           '''simple docstring'''

           if not isinstance(UpperCamelCase_				,    UpperCamelCase_ ) or top_k <= 0:
                  raise ValueError(f"""`top_k` has to be a strictly positive integer, but is {top_k}""" )

           __magic_name__      				=    max(UpperCamelCase_				,    UpperCamelCase_ )
           __magic_name__      				=    filter_value







    def __call__(		self :	List[str]				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    scores.shape
           __magic_name__      				=    jnp.full(batch_size * vocab_size				,    self.filter_value )

           __magic_name__      				=    min(self.top_k				,    scores.shape[-1] )  # Safety check
           __magic_name__				,  __magic_name__      				=    lax.top_k(UpperCamelCase_				,    UpperCamelCase_ )
           __magic_name__      				=    jnp.broadcast_to((jnp.arange(UpperCamelCase_ ) * vocab_size)[:, None]				,    (batch_size, topk) ).flatten()
           __magic_name__      				=    topk_scores.flatten()
           __magic_name__      				=    topk_indices.flatten() + shift

           __magic_name__      				=    next_scores_flat.at[topk_indices_flat].set(UpperCamelCase_ )
           __magic_name__      				=    next_scores_flat.reshape(UpperCamelCase_				,    UpperCamelCase_ )
           return next_scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	List[str]				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    bos_token_id







    def __call__(		self :	Optional[Any]				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    jnp.full(scores.shape				,    -float('inf' ) )

           __magic_name__      				=    1 - jnp.bool_(cur_len - 1 )

           __magic_name__      				=    jnp.where(UpperCamelCase_				,    new_scores.at[:, self.bos_token_id].set(0 )				,    UpperCamelCase_ )

           return scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	int				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    max_length
           __magic_name__      				=    eos_token_id







    def __call__(		self :	Optional[Any]				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    jnp.full(scores.shape				,    -float('inf' ) )

           __magic_name__      				=    1 - jnp.bool_(cur_len - self.max_length + 1 )

           __magic_name__      				=    jnp.where(UpperCamelCase_				,    new_scores.at[:, self.eos_token_id].set(0 )				,    UpperCamelCase_ )

           return scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	List[str]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           if not isinstance(UpperCamelCase_				,    UpperCamelCase_ ) or min_length < 0:
                  raise ValueError(f"""`min_length` has to be a positive integer, but is {min_length}""" )

           if not isinstance(UpperCamelCase_				,    UpperCamelCase_ ) or eos_token_id < 0:
                  raise ValueError(f"""`eos_token_id` has to be a positive integer, but is {eos_token_id}""" )

           __magic_name__      				=    min_length
           __magic_name__      				=    eos_token_id







    def __call__(		self :	Tuple				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    1 - jnp.clip(cur_len - self.min_length				,    0				,    1 )

           __magic_name__      				=    jnp.where(UpperCamelCase_				,    scores.at[:, self.eos_token_id].set(-float('inf' ) )				,    UpperCamelCase_ )

           return scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	List[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    list(UpperCamelCase_ )
           __magic_name__      				=    begin_index







    def __call__(		self :	Tuple				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    1 - jnp.bool_(cur_len - self.begin_index )

           __magic_name__      				=    jnp.where(UpperCamelCase_				,    scores.at[:, self.begin_suppress_tokens].set(-float('inf' ) )				,    UpperCamelCase_ )

           return scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	Union[str, Any]				,    UpperCamelCase_ :	list ):





           '''simple docstring'''

           __magic_name__      				=    list(UpperCamelCase_ )







    def __call__(		self :	Tuple				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    scores.at[..., self.suppress_tokens].set(-float('inf' ) )

           return scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	Any				,    UpperCamelCase_ :	Tuple ):





           '''simple docstring'''

           __magic_name__      				=    dict(UpperCamelCase_ )
           # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
           # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
           # Indexes without forced tokens will have a negative value.
           __magic_name__      				=    jnp.ones((max(force_token_map.keys() ) + 1)				,    dtype=jnp.intaa ) * -1
           for index, token in force_token_map.items():
                  if token is not None:
                         __magic_name__      				=    force_token_array.at[index].set(UpperCamelCase_ )
           __magic_name__      				=    jnp.intaa(UpperCamelCase_ )







    def __call__(		self :	List[str]				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	jnp.ndarray				,    UpperCamelCase_ :	int ):





           '''simple docstring'''

           def _force_token(UpperCamelCase_ :	Optional[Any] ):
                  __magic_name__      				=    scores.shape[0]
                  __magic_name__      				=    self.force_token_array[generation_idx]

                  __magic_name__      				=    jnp.ones_like(UpperCamelCase_				,    dtype=scores.dtype ) * -float('inf' )
                  __magic_name__      				=    jnp.zeros((batch_size, 1)				,    dtype=scores.dtype )
                  __magic_name__      				=    lax.dynamic_update_slice(UpperCamelCase_				,    UpperCamelCase_				,    (0, current_token) )
                  return new_scores

           __magic_name__      				=    lax.cond(
               cur_len >= self.force_token_array.shape[0]				,    lambda: scores				,    lambda: lax.cond(
                   self.force_token_array[cur_len] >= 0				,    lambda: _force_token(UpperCamelCase_ )				,    lambda: scores				,    )				,    )
           return scores



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	List[Any]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    generate_config.eos_token_id
           __magic_name__      				=    generate_config.no_timestamps_token_id
           __magic_name__      				=    generate_config.no_timestamps_token_id + 1

           __magic_name__      				=    decoder_input_length + 1

           if generate_config.is_multilingual:
                  # room for language token and task token
                  self.begin_index += 2
           if hasattr(UpperCamelCase_				,    'max_initial_timestamp_index' ):
                  __magic_name__      				=    generate_config.max_initial_timestamp_index
           else:
                  __magic_name__      				=    model_config.vocab_size
           if self.max_initial_timestamp_index is None:
                  __magic_name__      				=    model_config.vocab_size







    def __call__(		self :	str				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    scores.at[:, self.no_timestamps_token_id].set(-float('inf' ) )

           def handle_pairs(UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	List[Any] ):
                  __magic_name__      				=    jnp.where((cur_len - self.begin_index) >= 1				,    UpperCamelCase_				,    UpperCamelCase_ )
                  __magic_name__      				=    jnp.where(
                      input_ids_k[cur_len - 1] >= self.timestamp_begin				,    True and last_was_timestamp				,    UpperCamelCase_				,    )

                  __magic_name__      				=    jnp.where((cur_len - self.begin_index) < 2				,    UpperCamelCase_				,    UpperCamelCase_ )
                  __magic_name__      				=    jnp.where(
                      input_ids_k[cur_len - 2] >= self.timestamp_begin				,    UpperCamelCase_				,    UpperCamelCase_				,    )

                  return jnp.where(
                      UpperCamelCase_				,    jnp.where(
                          penultimate_was_timestamp > 0				,    scores_k.at[self.timestamp_begin :].set(-float('inf' ) )				,    scores_k.at[: self.eos_token_id].set(-float('inf' ) )				,    )				,    UpperCamelCase_				,    )

           __magic_name__      				=    jax.vmap(UpperCamelCase_ )(UpperCamelCase_				,    UpperCamelCase_ )

           __magic_name__      				=    jnp.where(cur_len == self.begin_index				,    UpperCamelCase_				,    UpperCamelCase_ )
           __magic_name__      				=    jnp.where(
               self.max_initial_timestamp_index is not None				,    True and apply_max_initial_timestamp				,    UpperCamelCase_				,    )

           __magic_name__      				=    self.timestamp_begin + self.max_initial_timestamp_index

           __magic_name__      				=    jnp.where(
               UpperCamelCase_				,    scores.at[:, last_allowed + 1 :].set(-float('inf' ) )				,    UpperCamelCase_				,    )

           # if sum of probability over timestamps is above any other token, sample timestamp
           __magic_name__      				=    jax.nn.log_softmax(UpperCamelCase_				,    axis=-1 )

           def handle_cumulative_probs(UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Optional[int] ):
                  __magic_name__      				=    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]				,    axis=-1 )
                  __magic_name__      				=    jnp.max(logprobs_k[: self.timestamp_begin] )
                  return jnp.where(
                      timestamp_logprob > max_text_token_logprob				,    scores_k.at[: self.timestamp_begin].set(-float('inf' ) )				,    UpperCamelCase_				,    )

           __magic_name__      				=    jax.vmap(UpperCamelCase_ )(UpperCamelCase_				,    UpperCamelCase_ )

           return scores







"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 							A						(				__snake_case: Optional[int]  )    ->		Tuple:

       """simple docstring"""





       for param in module.parameters():
              __magic_name__      				=    False


def 							A						(				)    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              __magic_name__      				=    'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'  )
       return device


def 							A						(				__snake_case: int  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    plt.imshow(__snake_case  )
       fig.axes.get_xaxis().set_visible(__snake_case  )
       fig.axes.get_yaxis().set_visible(__snake_case  )
       plt.show()






def 							A						(				)    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    datetime.now()
       __magic_name__      				=    current_time.strftime('%H:%M:%S'  )
       return timestamp